{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_corr_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Preventing error from occuring: XGBoost causes kernel to die.\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "              nthread=None, objective='multi:softprob', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instatiating the model classifier\n",
    "clf = xgb.XGBClassifier()\n",
    "\n",
    "# Fitting to the Data\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.29      0.22      0.25         9\n",
      "         Buy       0.22      0.40      0.29         5\n",
      "        Hold       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.24        17\n",
      "   macro avg       0.17      0.21      0.18        17\n",
      "weighted avg       0.22      0.24      0.22        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to tune\n",
    "params = {\"booster\": [\"gbtree\", \"gblinear\", 'dart'],\n",
    "          \"eta\": [.1, .5, .9],\n",
    "          \"gamma\": [0, 1, 3],\n",
    "          \"n_estimators\": [50, 100, 200],\n",
    "          \"max_depth\": [1, 3, 6],\n",
    "          \"grow_policy\": ['depthwise', 'lossguide']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 486 candidates, totalling 1458 fits\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.798, test=0.418), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.816, test=0.344), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.927, test=0.364), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.913, test=0.328), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.961, test=0.264), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=1.000, test=0.364), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=1.000, test=0.333), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.245), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.231), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.429), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.252), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.294), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.473), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
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      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=1.000, test=0.208), total=   0.1s\n",
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     ]
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      "[CV]  booster=gbtree, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.2s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.602, test=0.374), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.513, test=0.234), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n"
     ]
    },
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     "text": [
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.513, test=0.234), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.893, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.965, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.928, test=0.365), total=   0.1s\n",
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      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
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      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
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     "text": [
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n"
     ]
    },
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     "text": [
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.4s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.798, test=0.418), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.816, test=0.344), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.913, test=0.328), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.429), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.252), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.473), total=   0.2s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=1.000, test=0.208), total=   0.1s\n",
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      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.268), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.178), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.402), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.798, test=0.418), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.816, test=0.344), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.927, test=0.364), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n"
     ]
    },
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     "text": [
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.913, test=0.328), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.961, test=0.264), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=1.000, test=0.364), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=1.000, test=0.333), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=1.000, test=0.245), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=1.000, test=0.231), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=1.000, test=0.429), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.252), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.294), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.294), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.443), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.264), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.249), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.479), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.219), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.268), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.473), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.178), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.402), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.893, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.845, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
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     "text": [
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.965, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.602, test=0.374), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.533, test=0.190), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.513, test=0.234), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.533, test=0.190), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.650, test=0.312), total=   0.0s\n",
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      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.3s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.928, test=0.365), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.965, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
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      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.2s\n",
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      "[CV]  booster=gbtree, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.3s\n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.961, test=0.264), total=   0.2s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=1.000, test=0.333), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.245), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.231), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.429), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.252), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.294), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.473), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
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     "text": [
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=1.000, test=0.208), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=1.000, test=0.294), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=1.000, test=0.443), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=1.000, test=0.264), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=1.000, test=0.249), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=1.000, test=0.479), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.219), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.268), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.178), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.402), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.798, test=0.418), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.816, test=0.344), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=1.000, test=0.245), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.252), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.294), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.294), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.443), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.264), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.249), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.479), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.219), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.268), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.402), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.650, test=0.312), total=   0.1s\n",
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      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.893, test=0.208), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.928, test=0.325), total=   0.0s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.0s\n",
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      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.928, test=0.365), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.602, test=0.374), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.513, test=0.234), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.533, test=0.190), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.650, test=0.312), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.513, test=0.234), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.893, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.928, test=0.325), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.965, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.928, test=0.365), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.3s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.3s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.3s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.3s\n",
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      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.6s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.201, test=0.208), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.2s\n",
      "[CV] booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gbtree, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.2s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
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      "[CV]  booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV]  booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
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     ]
    },
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     "text": [
      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n"
     ]
    },
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     "text": [
      "[CV]  booster=gblinear, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.376, test=0.233), total=   0.1s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.404, test=0.262), total=   0.1s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.354, test=0.253), total=   0.1s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.404, test=0.262), total=   0.1s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n"
     ]
    },
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      "[CV]  booster=gblinear, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
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      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
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      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
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      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.354, test=0.253), total=   0.1s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
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      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.376, test=0.233), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.404, test=0.262), total=   0.0s\n",
      "[CV] booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=gblinear, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.354, test=0.253), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.798, test=0.418), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.816, test=0.344), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.927, test=0.364), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.913, test=0.328), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.961, test=0.264), total=   0.2s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=1.000, test=0.364), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=1.000, test=0.333), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.245), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.231), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=1.000, test=0.264), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=1.000, test=0.479), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.219), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.268), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.178), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.2s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.402), total=   0.2s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.927, test=0.364), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
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      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=1.000, test=0.429), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.252), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.294), total=   0.1s\n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.1, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.602, test=0.374), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.533, test=0.190), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.513, test=0.234), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.533, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.650, test=0.312), total=   0.1s\n"
     ]
    },
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     "text": [
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.513, test=0.234), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.893, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.928, test=0.325), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.965, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.928, test=0.365), total=   0.2s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.533, test=0.190), total=   0.1s\n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.893, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.928, test=0.365), total=   0.1s\n",
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      "[CV] booster=dart, eta=0.1, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
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      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.2s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.3s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.2s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV]  booster=dart, eta=0.1, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.2s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.798, test=0.418), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.816, test=0.344), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.927, test=0.364), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.913, test=0.328), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.961, test=0.264), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=1.000, test=0.364), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.245), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.231), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.429), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
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      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.178), total=   0.2s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.2s\n",
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      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.0s\n",
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      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.294), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.294), total=   0.2s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.443), total=   0.2s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.264), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
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      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.479), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.219), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.268), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.178), total=   0.2s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.2s\n",
      "[CV] booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.402), total=   0.2s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.602, test=0.374), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.4s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.928, test=0.365), total=   0.2s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.602, test=0.374), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.533, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.650, test=0.312), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.513, test=0.234), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.533, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.650, test=0.312), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.513, test=0.234), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.2s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.928, test=0.365), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.965, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.2s\n",
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      "[CV] booster=dart, eta=0.5, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.205, test=0.200), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
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      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
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     "text": [
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.2s\n",
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      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
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      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.3s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
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      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
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      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.3s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.5, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.0s\n",
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      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.816, test=0.344), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
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      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
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      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.231), total=   0.0s\n",
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      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=1.000, test=0.429), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.252), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=1.000, test=0.294), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
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      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=1.000, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=1.000, test=0.294), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=1.000, test=0.443), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
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      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.219), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.268), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
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      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.178), total=   0.2s\n",
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      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=1.000, test=0.402), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.863, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.798, test=0.418), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.816, test=0.344), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.898, test=0.258), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.927, test=0.364), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.913, test=0.328), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.961, test=0.264), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n"
     ]
    },
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     "text": [
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=1.000, test=0.364), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=1.000, test=0.333), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=1.000, test=0.245), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=1.000, test=0.231), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=1.000, test=0.429), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.252), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.294), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.294), total=   0.3s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=1.000, test=0.443), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.264), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.249), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=1.000, test=0.479), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.219), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.268), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=1.000, test=0.473), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.178), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.484), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=0, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=1.000, test=0.402), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.602, test=0.374), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.513, test=0.234), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.533, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.650, test=0.312), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.513, test=0.234), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.893, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.845, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.928, test=0.365), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.965, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.602, test=0.374), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.533, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.650, test=0.312), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.513, test=0.234), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.533, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.650, test=0.312), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.513, test=0.234), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.845, test=0.190), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.893, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.928, test=0.325), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.845, test=0.190), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.928, test=0.325), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.799, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.898, test=0.217), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.928, test=0.365), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.965, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.898, test=0.217), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.928, test=0.365), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.833, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.898, test=0.217), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.928, test=0.365), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  booster=dart, eta=0.9, gamma=1, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.833, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=50, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=100, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.2s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=depthwise, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=50, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=100, score=(train=0.205, test=0.200), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=1, n_estimators=200, score=(train=0.205, test=0.200), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=3, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.467, test=0.212), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.201, test=0.208), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=50, score=(train=0.455, test=0.269), total=   0.0s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=100, score=(train=0.455, test=0.269), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.467, test=0.212), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.201, test=0.208), total=   0.1s\n",
      "[CV] booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200 \n",
      "[CV]  booster=dart, eta=0.9, gamma=3, grow_policy=lossguide, max_depth=6, n_estimators=200, score=(train=0.455, test=0.269), total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 1458 out of 1458 | elapsed:  1.5min finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                     colsample_bylevel=1, colsample_bynode=1,\n",
       "                                     colsample_bytree=1, gamma=0,\n",
       "                                     learning_rate=0.1, max_delta_step=0,\n",
       "                                     max_depth=3, min_child_weight=1,\n",
       "                                     missing=None, n_estimators=100, n_jobs=1,\n",
       "                                     nthread=None, objective='multi:softprob',\n",
       "                                     random_state=0, reg_alpha=0, reg_la...\n",
       "                                     scale_pos_weight=1, seed=None, silent=None,\n",
       "                                     subsample=1, verbosity=1),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'booster': ['gbtree', 'gblinear', 'dart'],\n",
       "                         'eta': [0.1, 0.5, 0.9], 'gamma': [0, 1, 3],\n",
       "                         'grow_policy': ['depthwise', 'lossguide'],\n",
       "                         'max_depth': [1, 3, 6],\n",
       "                         'n_estimators': [50, 100, 200]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.5920878583129973\n",
      "Mean Testing Score: 1.0\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'booster': 'gbtree',\n",
       " 'eta': 0.1,\n",
       " 'gamma': 0,\n",
       " 'grow_policy': 'depthwise',\n",
       " 'max_depth': 6,\n",
       " 'n_estimators': 200}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, eta=0.1, gamma=0,\n",
       "              grow_policy='depthwise', learning_rate=0.1, max_delta_step=0,\n",
       "              max_depth=6, min_child_weight=1, missing=None, n_estimators=200,\n",
       "              n_jobs=1, nthread=None, objective='multi:softprob',\n",
       "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "              seed=None, silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.29      0.22      0.25         9\n",
      "         Buy       0.20      0.40      0.27         5\n",
      "        Hold       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.24        17\n",
      "   macro avg       0.16      0.21      0.17        17\n",
      "weighted avg       0.21      0.24      0.21        17\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bh2c1Pom3bI4JP0vXCXYTwbFPndtfknT/QwMWWIyZt3Ni/fmMbV0MwFODruOt8tMp8YYu2ztFtFvKYyslZkaf/iHKaoKJPeqKzl1bm4czb/lFfObQmzlu3BNce8y13Lp8NluahzNlxAq+fMQNVJU2s6O1MjzgQaJiPdh2Mtu0Y1aMDUqsYWLLe3sIT2+8gumNV6Rsv71oAneOeD0boUkGdHRFtzXs4O27/xJxNNIf81ZcyNjB6zlzv/mcNGERJ01YtNvzO1ormbPkap0lHCWL5xIlJWGRCJQOH8pep84A4O27/0L7Du2ilNuM7z99JU+sn85H9r+HyTUvU1XayKamkTy94QPcueo81jXURh2kxJCScIw1FE9k7iht3JCPWrfUMX/o4VGHIRm2eO0MFq+dEXUY0iXtmCUiIhINY0BmN/eXkrCIiBSEOFbC8ftaICIiUiBUCYuISN4zDLP41Z1KwiIikv8MiGF3tJKwiIgUhDiuE45fRCIiIgVClbCIiBSEOM6OVhIWEZH8F5xlGHUUe4hfRCIiIgVClbCIiBQEdUeLiIhEJYazo5WERUQk75lZLM8Tjt/XAhERkQKhSlhERAqDuqNFRESioYlZIiIiUdA6YREREUmmSlhERAqDuqNFRESiofOERUREohDT84Tj97VARESkQKgSFhGRAmCY1gmLiIhERNtWioiISAdVwiIikv8MbVspIiISDYtld7SSsIiIFIQ4TsyKX0QiIiIFQpWwiIjkPyNrBziY2RvAdqAdaHP3aanaKgmLiEgBsGzvmHWCu2/qrpGSsIiI5D0jnntHxy8iERGR+BppZkuTbpd10caBB83s2RTP76JKWERE8l/mDnDYlG6MN3Ssu683s72Ah8xslbsv7qqhKmERESkAFkzM6u+tB9x9ffjfd4G7gCNTtVUSFhERyRAzG2RmQzruA6cA/0jVXt3RIiJSGLKzY9Zo4C4LPqsE+LW7L0jVWElYREQKQxZ2zHL314BDe9peSVhERPKfWdY26+iN+EUkIiJSIFQJi4hIYcjujlk9oiQsIiKFIYbd0UrCIiJSGGJ4nnD8vhaIiIgUCFXCIiKS/8yyskSpt5SERUSkMMSwO1pJWERECkMMJ2bFLyIREZECoUpYRETyn8aEB15VWTuHjq+LOgzJoBcfWBV1CJJpP+7yWFWRgRfDMeH4fS0QEREpEHlVCYuIiKQUw4lZSsIiIlIALJbd0UrCIiKS/4xYTsyKX0QiIiIFQpWwiIjkPQdc3dEiIiJRME3MEhERiUwMk3D8IhIRESkQqoRFRKQgaExYREQkChbPMeH4RSQiIlIgVAmLiEhhUHe0iIhIRGK4Y5aSsIiIFACL5cSs+H0tEBERKRCqhEVEJP8ZsZwdrSQsIiIFwZWERUREohDP84Tj97VARESkQKgSFhGRgqDuaBERkajEsDtaSVhERPKf9o4WERGRZKqERUQk7zk6ylBERCQ66o4WERGRDqqERUSkIDjqjhYREYmAaZ2wiIhIZLKUhM2sGFgKrHP3M9K1jd/XAhERkdz2JWBlTxoqCYuISP6zYIlSf2/dfozZOOB04OaehKXuaBERyXuevTHh64ErgSE9aaxKWERECoNZ/28w0syWJt0ue+/t7QzgXXd/tqchqRIWERHpuU3uPi3Fc8cCZ5nZLKACqDaz2939olRvpkpYREQKgltRv29p39/96+4+zt0nAucDj6RLwKBKWERECoJpsw4REZGoZHOzDnd/FHi0u3bqjhYREYmIKmEREcl/Rsfs5ljpUyVsZl/OdCAiIiIDx3CK+n3LtL6+41cyGoWIiEgB6mt3dPxqehERkRQcerTtZLb1NQl7RqMQEREZYDl1lKGZbafrZGtA1YBFJCIiMgByap2wu/do82kZOGUta6nZNp9BO56nqmkFpW0bKWnbjFsZO8vGUz9kBu+M+hTNFZOiDlV6odibGN+ygHEtCxnVtpTq9lcp9UZ22jC2lEzl9fJzeKniUtqtMupQpZdm1D7G2Qfcw6SaVxhU2sCmppE8teFIfrPqXNY11EYdnsRQr7qjzWwQcDbwcXc/fWBCkg412+Yzce1X93zCW6hqXklV80r22nQLb9X+B+/sddme7SSWZm/emzLfvsf1St9Ebesj1LY+wkFNN7Kw+m7qSw6IIELpPedrR17HmfvN3+1q7eANnHPA3Zw28UHmLLmaJzccHVF8QvZOUeqVbpOwmZUBs4CPA6cBfwT+b4DjEiBRVMnW6lOoH/JBdlRNpaV0b9pKRlDa+i6DG59h7DvXU7HzNSau/So7yyewbeipUYcsPVDm22mjnDfKz+GNsrPYWPoBdloNgxNv8b6mnzGl+WfUtK9iVt2p/GH4MtpscNQhSzcunnLHrgT88JsncOuKi9jcNIKDRy7nS++/gbGD3+bbx17LJxfOZc328RFHW7hyamKWmZ0MXACcCiwCbgOOdPdLsxRbwds4cjYbR87e43pbyXCaKg9ky7CzmLpyOmWtGxjzzg1KwjliecVneW7QVTQVjd7t+paiGpYMuYmG4gkc1fh1qhNvcFDTTbxYdWVEkUpP1FRsYfaU2wFYsu5o5jwxh44FJI+vO5ZXt+3LvJmXUlXaxGVTb+aqJddGGG3hcuI5JpyuNl8I7Acc5+4Xufu9QCI7YUlPtJcMZcuwMwEYtOPFiKORnloy5MY9EnCyZZVX0GwjABjfsiBbYUkfzZz4IFWlzQDMXfZpOq/g3NA4hntfPQOA48c9Rk3FlmyHKDGWLgkfAfwN+IuZPWRmnwKKsxOW9JRbKQCJovKII5FMcSuhrjgYC65KrI84GunOsbVPAPBW/The2bZ/l20WrTkegOKiBNPHPJW12CSJ2YAfZdgXKd/R3Z9396+6+37ANcDhQJmZPWBmmgUUA5ZopqbuAQAaqw6LOBrJpMrEOwC0WHXEkUh3JtW8DMCKze9L2WbVlsm0JYJft5OHr85KXLInD48z7M8t03qU1t19ibt/HqgFrgemZzwS6RlPUNr6NsO2zWfK6tOp2PkaCStj7ZivRx2ZZMiI1uepTrwOwLslR0UcjaQzsnIjVaVNAKxvGJuyXWuijM1NwRDDhOq3shKb5IZ0E7Pe3+mSA5vcfSHBeLFk0eRXPsaw+of3uN5UfgCv7/M/NA46IoKoZCAc1RhMxHKMlZXqdIqzYeV1u+5v2zk0bdutO2sYPWgj1WX1Ax2WpJBrS5T+u4trw8MlS+e7e69mAplZO/B3glkL7cDn3f2J3ryH7K61ZCRv73U5jVWHRh2KZMjUHdcxrjX4srWi4nK2lhwScUSSTkVJ8677Le1ladvuDJ+vLGka0JgktTjOjk63Y9YJXV03s2nADcAHe/lZTe5+WPgepwLfA47v5XsUrNX73o55G0aCkratDGl4krHvXM8/rbmC0Rt/zur9fsPO8glRhyn9MK5lIUc2fgOAzcWH8LfBP4w4IumOJe3s290veOvinmSPx3Szjl5H5O5Lgf7uHlANbAUwsw+Z2X0dT5jZjWZ2iZmdaGZ3JV0/2cz+1M/PzVleVEGieDDtxdXsLJ/AphHn8/cDF9FQNY2q5lVMevUCcK0gy1UjW5/lpPpzKaKdhqLxLBh6n7atzAFNbe/9jMqLd6ZtW1bcEr6mYkBjktzS6yRsZqPp2ylKlWb2gpmtAm4GvtNN+0eA95nZqPDxpcAtffjcvOVFlbxVezUAVc0rqd6+OOKIpC+Gtq1mZt0synw7TTaK+4cupLFYuyrlguRx4OTx4a4MK98GQH2LZrxHJY6zo9NNzLqBPZPtcOAY4Et9+Kzk7ujpwDwzOzhVY3d3M7sNuMjMbiGYkX1xF3FeBlwGsPfYcX0IK7c1JE3IGtS0jPrqD0UXjPTaoPY1zKo7hUrfSItVM3/oA9SVHBh1WNJDm5pGsaO1kqrSJsYM3pCyXWlRCyMrNwHwZv0+2QpPOsmpbSuBpZ0eO7AZ+Iq7v9ufD3X3J81sJDAKaGP3ijy5r+YW4F6gGfi9u7d18V5zgbkA7zv48II759iS/kjiOOlAUqtIbOT0ulMYkniLNipZUH0Pm0s7L0qQuFu99QAO22sZB41YmbLN5OGrKSkKhote2qJTz6LiHr/fkemS8AnufslAfKiZHUiw+9Zm4E1gipmVEyTgE4HHAdx9vZmtB74FnDwQseS66ob3JpjvLJsYXSDSK6WJembWzWRY+0u0U8pD1b/n7bLeznWUOFiy7hgO22sZ+1SvYd+hr/Fa3b57tDlh/F8BaE8U8eQGrf2W96QbE56a4c/qGBN+Afgt8Al3b3f3NcDvgGXAHcDznV53B7DG3VdkOJ7Yq2hOv7NOcds2xq+7BoD2oiHUV2uyeS4o9mZOrT+LUW3PkaCIRUPmsaZ8VtRhSR898MYpuyZbfWbqzXs8v/egDZy1370A/HXtDLY2D89qfNLBcIr6fcu0dJVwlZkdTor59O7+XG8+yN1T7jvt7lcCqY6KOQ74eW8+K19MXXEMW4eextZhZ9BYdSitpXvhFFHWuoHq7Y8x5p0bKG9dB8Ca2qtoL9aEj7gzb+fE+vMZ2xpMontq0HW8VX46Jd7QZXuniHarymaI0ktbm4czb/lFfObQmzlu3BNce8y13Lp8NluahzNlxAq+fMQNVJU2s6O1MjzgQaIQ11OU0iXhWoINO7qK2oEPD0hESczsWaARuGKgPyuOjHaG193P8Lr7U7ZJWAVrxl7FO6P+NYuRSV8NSqxhYss9ux5Pb7yC6Y2p/3pvL5rAnSNez0Zo0g/zVlzI2MHrOXO/+Zw0YREnTVi02/M7WiuZs+RqnSUcsVxLwq+4+4An2nTcvaD3Ylwx6X6qtz/GkIYnKG9ZQ2nrRsxbaC+upqliEvWDZ7Bx5IW0lOkftki0jO8/fSVPrJ/OR/a/h8k1L1NV2simppE8veED3LnqPNY11EYdpMRQuiQsEds++Bi2Dz4m6jAkgxqKJzJ3lDZVyVeL185g8doZUYchKcSxEk43yvw9M5vS+aKZHZS0gYaIiEgO6P9GHdk+yvAcgnW8nY0D/jfjkYiIiBSYdEn4EHf/a+eL4VGGmV6+JCIiMqDcrd+3TEs3JpzuXK7STAciIiIyUOK6RCldJbzazPbYQcDMZgKvDVxIIiIimRfHMeF0lfC/AfeZ2bnAs+G1aQQHKZyR8UhEREQKTMpK2N1XA4cAfwUmAhOAR4FP0rdTlERERCKTa5Uw7r4TuCXcvvIC4GrgdeCPGY9ERERkwAzMxKr+Snee8CTgfILku5ng0AVz9xOyFJuIiEhGOJCI4cSsdJXwKuAx4Ex3fwXAzP4tK1GJiIgUgHSzoz8KvA0sMrOfm9mJpDhRSUREJO7iOCacbmLWXe5+HnAgwYSsfwNGm9lPzeyUjEciIiIyUDyem3V0e0Kxuze6+x3ufgbBlpUvAF/LeCQiIiIFptsknMzdt7j7z6I+4lBERKS3stEdbWYVZva0mb1oZsvN7Np07XWUoYiIFICsLVHaCXzY3RvMrBR43MwecPe/ddVYSVhERPJetvaOdncHGsKHpeHNU7XvVXe0iIiIpGdmxWb2AvAu8JC7P5WqrZKwiIgUhAzNjh5pZkuTbpft+Tne7u6HEUxmPtLMDk4Vk7qjRUSkICQy8zab3H1aTxq6+zYzexQ4DfhHV21UCYuISEHIxjphMxtlZsPC+5XASQQ7UHZJlbCIiEjmjAFuNbNigkL3d+5+X6rGSsIiIpL3BmrbyT0+x30ZcHhP2ysJi4hIQcipowxFRETySTYq4d7SxCwREZGIqBIWEZH855BIuW9VdJSERUQk72Vr28reUne0iIhIRFQJi4hIQdDsaBERkYi4xoRFRESiYCQ0JiwiIiIdVAmLiEjeczQmLCIiEhmNCYuIiERE64RFRERkF1XCIiKS/7RtpYiISDTiOjFL3dEiIiIRUSUsIiIFQbOjRUREIhLHHbOUhEVEpCDEsRLWmLCIiEhEVAmLiEjecyyWs6OVhEVEJP9pnbCIiEh0NCYsIiIiu6gSFhGRghDHAxyUhEVEJO85GhMWERGJTBzHhPMqCe9oKebFNUOjDkMy6LnntkYdgojIgMmrJCwiIpKKKmEREZEIuEMihpt1aImSiIhIRFQJi4hIQV3PCtUAABOWSURBVFB3tIiISESUhEVERCISx3XCGhMWERGJiCphERHJew46ylBERCQSrjFhERGRyGhMWERERHZRJSwiInkvGBOOOoo9qRIWEZGC4N7/W3fMbLyZLTKzlWa23My+lK69KmEREZHMaQOucPfnzGwI8KyZPeTuK7pqrCQsIiIFIRsTs9x9A7AhvL/dzFYCtYCSsIiIFKgIliiZ2UTgcOCpVG2UhEVEJO85kEhk5K1GmtnSpMdz3X1u50ZmNhj4I/Bld69P9WZKwiIiIj23yd2npWtgZqUECfgOd/9TurZKwiIiUhCy0R1tZgb8Aljp7j/qrr2WKImISEHIxhIl4FhgNvBhM3shvM1K1ViVsIiI5D33rM2Ofhzo8UkRqoRFREQiokpYREQKgsdw30olYRERKQgxzMFKwiIiUhgytE44ozQmLCIiEhFVwiIikvd6scQoq5SERUSkIGRjiVJvqTtaREQkIqqERUSkIKg7WkREJCIew/5oJWEREcl72dq2src0JiwiIhIRVcIiIlIQNCYsIiISkUQM+6OVhEVEJO858ayENSYsIiISEVXCIiKS/7RtpYiISFScRAyzsLqjRUREIqJKWERECoLH8DxhJWEREcl7wezo+HVHKwmLiEj+c0jEsBLWmLCIiEhEVAmLiEhBUHe09FqxNzG+ZQHjWhYyqm0p1e2vUuqN7LRhbCmZyuvl5/BSxaW0W2XUoUqPOXuXrGZi2XPBrfw5aktXUGottHo5X1i7PuoApY9m1D7G2Qfcw6SaVxhU2sCmppE8teFIfrPqXNY11EYdXkFz4nmKkpJwzM3evDdlvn2P65W+idrWR6htfYSDmm5kYfXd1JccEEGE0lsjitdwzZhjog5DMsr52pHXceZ+83e7Wjt4A+cccDenTXyQOUuu5skNR0cUn+DxPE9YY8IxV+bbaaOcV8ov4C9D7uTO4a/wqxGb+UPN8yyvuBzHqGlfxay6UynxhqjDlV7a2jaG53eczsvN06MORfrh4il37ErAD795Ahc/8AtO/9Of+eri77K+YW+qSpv49rHXMn7ImogjlbhRJRxzyys+y3ODrqKpaPRu17cU1bBkyE00FE/gqMavU514g4OabuLFqisjilR6qiFRw0833sbrLUdQnwh+rmdU/4ADKp6MODLpi5qKLcyecjsAS9YdzZwn5gAGwOPrjuXVbfsyb+alVJU2cdnUm7lqybURRlvYYjgkrEo47pYMuXGPBJxsWeUVNNsIAMa3LMhWWNIPO30ILzbP2pWAJbfNnPggVaXNAMxd9mk6EnCHDY1juPfVMwA4ftxj1FRsyXaIEkokvN+3TFMSznFuJdQVB2PBVQlN6BHJtmNrnwDgrfpxvLJt/y7bLFpzPADFRQmmj3kqa7HJe9w9I7dMUxLOA5WJdwBoseqIIxEpPJNqXgZgxeb3pWyzastk2hLBr9vJw1dnJS7JDUrCOW5E6/NUJ14H4N2SoyKORqSwjKzcSFVpEwDrG8ambNeaKGNzUzBsNKH6razEJnvyRP9vmaaJWTnuqMZgIpZjrKy8LOJoRArLsPK6Xfe37Ryatu3WnTWMHrSR6rL6gQ5LUiioowzNrKHT40vM7MZuXnONmf17F9cnmtk/Mh1jrpu64zrGtT4MwIqKy9lackjEEYkUloqS5l33W9rL0rbdGT5fWdI0oDFJblElnKPGtSzkyMZvALC5+BD+NviHEUckUniM9yor7zQres+2e96T7NK2lSEzmwD8EhgFbAQudfe3OrU5ImyzA3g860HG2MjWZzmp/lyKaKehaDwLht6nbStFItDU9t6/u/LinWnblhW3hK+pGNCYpGvuDMgSo/4ayIlZlWb2QscN+HbSczcC89x9KnAH8OMuXn8L8EV3T7uVkJldZmZLzWxpQ93GjAUfV0PbVjOzbhZlvp0mG8X9QxfSWDw+6rBEClLyOHDy+HBXhpVvA6C+RasYouLe/1umDWQSbnL3wzpuwJyk56YDvw7v3wYcl/xCMxsKDHP3vya16ZK7z3X3ae4+bfDQURkMP34Gta9hVt0pVPpGWqya+UMfoK7kwKjDEilYm5pGsaM1qIbHDN6Qsl1pUQsjKzcB8Gb9PlmJTXJDXJYodf5+YV1cK2gViY2cXncKQxJv0UYlC6rvYXPp+6MOS6Tgrd4abJZz0IiVKdtMHr6akqJgfctLWyZlJS7Zkye837dMiyoJPwGcH96/kE5jvu6+Dagzs+OS2hSs0kQ9M+tmMqz9Jdop5aHq3/N22QejDktEgCXrghOx9qlew75DX+uyzQnjg0699kQRT27Qev4ouDuJDNwyLaok/EXgUjNbBswGvtRFm0uBn5jZk0DBzukv9mZOrT+LUW3PkaCIRUPmsaZ8VtRhiUjogTdO2TXZ6jNTb97j+b0HbeCs/e4F4K9rZ7C1eXhW45P3xLESHrDZ0e4+uNPjXwG/Cu+/AXy4i9dck3T/WeDQpKev6dw+35m3c2L9+YxtXQzAU4Ou463y01MeWegU0W5V2QxR+mhMySoqit47J3pYcce+384/lT2zW9s1LVNpozyL0UlvbG0ezrzlF/GZQ2/muHFPcO0x13Lr8tlsaR7OlBEr+PIRN1BV2syO1srwgAfJZ2b2S+AM4F13P7i79lonHGODEmuY2HLPrsfTG69geuMVKdtvL5rAnSNez0Zo0k8X1FzJpIole1wvtRa+Ovq03a59c/3zbG7XZJ44m7fiQsYOXs+Z+83npAmLOGnCot2e39FayZwlV7Nmu1YyRGkgKtku/IpwBVBPGisJi4j0m/H9p6/kifXT+cj+9zC55mWqShvZ1DSSpzd8gDtXnce6htqogyxsDtnIwe6+2Mwm9rS9knCMNRRPZO6oAdgxXCL3o433dN9Ics7itTNYvHZG1GFIDlESFhGRvOdkrDt6pJktTXo8193n9vXNlIRFRKQAeKb2jt7k7tMy8UagJCwiIoWgAPeOFhERKShmdifwJDDZzNaa2afStVclLCIiBSEbRxm6+wW9aa8kLCIieS+DE7MySklYRETyn8czCWtMWEREJCKqhEVEpAAMzClI/aUkLCIiBSGO3dFKwiIikvec7MyO7i2NCYuIiERElbCIiOS/mO6YpSQsIiIFIY5jwuqOFhERiYgqYRERKQAZO0Upo5SERUQk77mDJxJRh7EHJWERESkIcZyYpTFhERGRiKgSFhGRgqAxYRERkSi4x3KJkpKwiIjkvbieJ6wxYRERkYioEhYRkYKQcC1REhERyT5Xd7SIiIgkUSUsIiJ5z9HsaBERkchonbCIiEgUHBIx3DtaY8IiIiIRUSUsIiIFQWPCIiIiEXAc1zphERGRCGidsIiIiCRTJSwiIgUhjpWwkrCIiBQA197RIiIiUXCNCYuIiEgyVcIiIlIQPIY7ZikJi4hI/lN3tIiIiCRTJSwiIgVAO2aJiIhEwoFEDLujlYRFRCT/eTwnZmlMWEREJCKqhEVEpAC4ZkeLiIhExT3R71t3zOw0M3vJzF4xs691116VsIiI5L8srBM2s2LgJ8DJwFrgGTO7x91XpHqNKmEREZHMOBJ4xd1fc/cW4DfAR9K9QJWwiIjkPcezMTu6FliT9HgtcFS6F5h7/Aaq+8rMNgJvRh1HFowENkUdhGSUfqb5p1B+phPcfVTUQXTHzBYQ/Ez6qwJoTno8193nhp/xL8Cp7v7p8PFs4Eh3/0KqN8urSjgX/iJkgpktdfdpUcchmaOfaf7RzzRe3P20LHzMWmB80uNxwPp0L9CYsIiISGY8AxxgZv9kZmXA+cA96V6QV5WwiIhIVNy9zcw+DywEioFfuvvydK9REs5Nc6MOQDJOP9P8o59pAXL3+cD8nrbPq4lZIiIiuURjwiIiIhFREhYREYmIknCOMTOLOgYRSc3MhkUdg+QOJeEcYmYfAC42s8qoY5HMMzNNlMxxZlYLLDGzD0cdi+QGJeHcMgj4PPBRM6uIOhjJHDObBPzUzMqjjkX6xszM3dcB1wHXmdnRUcck8acknAPM7BAzm+3ujwJXAJ8GzlUizn1JwwtlQIJgbaHkmDABdyw1eYNg+9y5ZnZMdFFJLlASzg2HAP9sZh9398XANcAnUSLOB9Xhf18CRgNXRxiL9FFHAg43avguwek5jwD/Z2YfjDI2iTcl4RjrqJLc/dfA74HTzeyisCK+hiARf0xjxLnJzMYB88zsU+7eSjDUMMjMJkQcmvSQmU02s5lJlw4ArnL33wFfAf4P+B8zOy6SACX2NBEkpjp1b+Hud5pZPTDbzHD3281sDvBjoBX4bVSxSu+Z2T4Ex579CPh3M5tKUAlXAgcCb3b+OyDxYmalwEeB2vBHtYDg53cR8Ii7J8zsEeAC4PtmdrK7N0UYssSQdsyKOTP7V2AfgqOzfgIcR7Ap+Hx3/7WZHQusdfdCOMIx55lZETAU+D7BuaP/DRgwjGC8/yRgO/Axd387qjilZ8xsb+BiYAzBF+GVBFsWPuXuXzGz84CDgRvd/Z3oIpW4UhKOGTOrcvcd4f0vAmcB3wauB/7o7t8Nz6y8GLgt7PaSmOtc1YZLWP4FeA24y91fCa8fBHwG+IW7vxhJsJJWFz/LUQRDQ+MJxoJfBv5IcITdIQRfqNJu4i+FS2PCMWJms4D/NLPxZlZM8I/6VGAa8DbBsocyd/898DNgSXTRSm+4u5vZoWZ2Q/j4EeBOgjHE88xs3/D6coIzSE+JLFhJKTkBm9mZZnYaMNndf0AwI/o8YB93Pw64BDhOCVjSURKOCTM7A/ge8Ki7ryFYrjIOeJSgC/oj7t4CfNLMznL3+8I1iRJTZrafmZ1jZmeHl1qB4Wb2P+Ev88XA/cDlwDlmNszMBhF0Tff4FBbJPjP7LEEP1XHAz83sm+5+HcHypMvN7ER33+Hum6OMU+JPSTgGwnGlK4BPu/ufzawi/Lb9K4KxptvdvdXMLgG+BOibdcyFm2/cDRwLXGlmn3T3FQTLV4YSDC8AvAg8Dyxw923u3gjMVPUUL2a2v5kNDXs09iIYSvi4u38LOIbgy/ElwM3AP4C/Rxet5BLNjo6HnQRVUnO47vdrZnY8wQSdLQSL/mcChwEfdfdXowtVumNmU4A7gK+7+71mdhFQbWYHuftyM/sv4Ltm9iRB1ftld/9HUldnS4ThSydmVgN8Dmgxs++5+7tmtpnw5+TuW83s34Bj3f1XZvZjd2+PMmbJHZqYFQPheuCvEIwDHgT8BXgcWAGcDawG7gKK3H1jVHFKz4RrQhe7e1H4eBmwDhgLPO/ul4TXTwfWufsLUcUqqXV8KQr/fZ5GUPG2A9cC/wmcDBzt7m1m9gXgaIIJkwktLZOeUiUcA+E/9J8BTxBMxrrb3XcCmNllwDKNLeUOd3/czE43s9cIZj//wd2/bWZlwN/N7Fvu/h/ufn/EoUp6xUAbQbHygJlVA1cCje7+dTMbAiwOv2QdBVyoClh6S5VwjIVLkb4GnKsu6NxjZicCC4Eyd0+E1z4FDHP3/440OEnLzEYCS4Ejw+7nsQS71r0INABb3f17ZvZ+gjH+N9z99egillyliVkxZGZjzOzLBFtTfkIJODe5+8ME67xXQzC5B/h/aNJO7Ln7JuALwCNmdjBwG/Brd/8swcz1vczsB8Ar7r5ICVj6St3R8bSNYMH/Rzo2cZDc5O7zzSxhZjuA1wkmYT0YdVzSvXBSXSuwDPiGu/8kfOoxoByYEf5XpM/UHS2SBWHXdLW73xV1LNI7ZnYycANwlLvXJV3ftbudSF8pCYtkkQ5lyE3hEsHrgenuviXqeCR/qDtaJIuUgHNTODu6DPiLmU0LLulnKf2nSlhEpIfMbLC7N0Qdh+QPJWEREZGIaImSiIhIRJSERUREIqIkLCIiEhElYRERkYhoiZJICmbWTrDFZAmwkmAL0R2drr8OzHb3bWY2MWz3UtLb/Mjd55nZGwRHU0JwMMCfgO+4+87wdfe5+8Hh5x4J/BAYDTjBiVrPA/8avn5K+BntwAJgFXAdwUlNHT4O7AjjWQVUhJ//E3e/tZ9/NCKSIZodLZKCmTW4++Dw/h3As+7+o07XbwVWu/t3OyfTTu/1BjDN3TeZ2WBgLtDq7p9Ifp2ZjQaeBs539yfDY/Q+Cjzm7u90fq/w8SXh4893+szd4jGzfQmS//+6+y0Z+mMSkX5Qd7RIzzwG7N/F9SeB2t68UbjO9HLgbDMb3unpzwG3uvuTYVt39z90JOD+cPfXCM6t/mJ/30tEMkNJWKQbZlYCzKTT6UdmVgycCNyTdHk/M3sh6Tajq/d093qCruwDOj11MPBsH8I8r9PnVqZo9xxwYB/eX0QGgMaERVKrNLMXwvuPAb/odH0iQcJ8KOk1r7r7YT18f8tIlIHfdtEdPdCfKSL9pEpYJLUmdz8svH3B3VuSrwMTgDKCLuReMbMhBEl8daenlgNH9CPm7hxOMFlLRGJASVikj8Jj7b4I/LuZlfb0deHErJuAP7v71k5P3wh8wsyOSmp/kZnt3d94w4laPyQ4lk9EYkDd0SL94O7Pm9mLwPkEXdb7JXVhA/zS3X8c3l8UznYuAu4CvtPF+71jZucDPzSzvYAEsJhgVnM655nZcUmPPwusD+N5nveWKN2gmdEi8aElSiIiIhFRd7SIiEhElIRFREQioiQsIiISESVhERGRiCgJi4iIRERJWEREJCJKwiIiIhH5/yD2mF3/9x+4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
